1. Field of the Invention
The present invention relates to the compression technology for image data and, more particularly, the lossy compressed coding for multi-level input image.
2. Description of the Related Art
Since normally image data have an enormous amount of data, in case of communication, store, and the like, the image data is compressed to reduce an amount of data. Coding approach of the image data is roughly classified into two types of a lossless coding system and a lossy coding system.
As for the latter, for example, abase line system defined by Joint Photographic Experts Group (referred simply to as JPEG hereinafter) is a typical compression system (For example, Endo; “International Standard Coding System of Color Still Image” Interface, December 1991, pp.160–167). Normally the lossy compression can control tradeoff between image quality and an amount of codes by coding parameters. In JPEG, a quantization table corresponds to the coding parameters.
The quantization table defines 8×8 quantizing steps in quantizing process executed in JPEG. If the quantization table is kept constant, the image quality and the amount of codes can be obtained at the same level when the images having similar characteristics are input. This is because the quantization is applied to a frequency component, i.e., a DCT (Discrete Cosine Transform) component. Thus, the similar quantized results can be derived from the image whose frequency component has the similar tendency.
However, it is normally known that, if the image which has the same contents but has different resolution are input, the image quality is largely different. More particularly, if the resolution is lower, the degradation of the image quality is more conspicuous in many cases. As a related art that intends to overcome such problem, an approach disclosed in Patent Application Publication (KOKAI) Hei 5-260308 will be explained as an example in the related art. This example in the related art is such a technology that relationships among the image quality, the resolution, and the quantization table are derived previously by the sensory evaluation and then the optimum quantization table is selected in compliance with the result and input conditions.
FIG. 11 is an example of a configuration of an image coding apparatus in the related art. The configuration and the terms are partially modified in line with purpose of the explanation of the present invention, but such modifications do not affect the essence of the invention. In FIG. 11, 10 is an image inputting portion, 20 is a DCT portion, 31 is a quantization feature storing portion, 32 is a quantization table setting portion, 40 is a quantizing portion, 50 is an entropy coding portion, 60 is a code outputting portion, 100 is image data, 110 is resolution data, 111 is quantization table designating data, 120 is DCT component data, 130 is quantized table data, 140 is quantized DCT component data, and 150 is coded data.
Each of portions of the image coding apparatus in FIG. 11 will be explained. The image inputting portion 10 receives the input data from the external device, and then sends out the data to the DCT portion 20 as the image data 100 and sends out the resolution to the quantization feature storing portion 31 as the resolution data 110. The DCT portion 20 applies DCT (Discrete Cosine Transform) to the image data 100, and then sends out the DCT component data 120 to the quantizing portion 40. The quantization feature storing portion 31 generates the quantization table designating data 111 based on stored information and the input resolution, and then sends out the quantization table designating data 111 to the quantization table setting portion 32. The quantization table setting portion 32 sends the quantized table data 130 to the quantizing portion 40 based on the quantization table designating data 111. The quantizing portion 40 applies the quantization process to the DCT component data 120 based on the quantized table data 130, and then sends out the quantized DCT component data 140 to the entropy coding portion 50. The entropy coding portion 50 executes entropy coding of the quantized DCT component data 140 by a predetermined method, and then sends out the coded data 150 to the code outputting portion 60. The code outputting portion 60 sends out the coded data 150 to the external device.
An operation of the example in the related art based on the above configuration will be explained. FIG. 12 is a flowchart showing an operation of the image coding apparatus in the related art. The operation of the example in the related art will be explained with reference to FIG. 12.
In S10, the image data are input into the image inputting portion 10. In 520, the DCT is executed in the DCT portion 20. In S31, desired subjective evaluation value and the resolution of the input image are searched from the stored information to obtain the corresponding quantization table. In S32, the quantization table searched in the quantization table setting portion 32 is supplied to the quantizing portion 40. In S40, the quantizing portion 40 executes the quantization by using the quantization table in S32. In S50, the entropy coding portion 50 applies the entropy coding to the quantized result in S40. In S60, the code outputting portion 60 sends out the coded data to the external. In S70, the coding process is finished if the image data are ended, otherwise the process goes to S10.
In the above operation, the order of S20, S31 and S32 may be set oppositely, otherwise they may be executed in parallel. Also, the quantization feature storing portion 31 stores the relationships among the objective evaluation value, the resolution, and the quantization table as information, and such information are obtained by the sensory evaluations that are carried out in advance. Also, as the entropy coding executed in S50, the Huffman coding and arithmetic coding are designated in JPEG. Since other details are well known in the above literature, etc., their explanations are omitted.
Then, problems in the example in the related art will be explained. In the example in the related art, relationship between the image quality and the resolution (referred to as “quantization feature” hereinafter) is obtained by the sensory evaluations. At first, problems of the sensory evaluations themselves will be explained. First, since normally a large number of images must be employed in the sensory evaluation by assigning a number of parameters, a great deal of time and labor are needed. Specifically, the parameters in the related art are the image type, the resolution, and the quantization table. Second, since evaluated results are varied by the evaluators, many evaluators must be prepared. Third, if subjectivity of the evaluator is different from subjectivity of the actual user of the system, it is impossible to get proper evaluation of the image quality from the user's point of view.
Next, problems in the configuration in the related art will be explained. First, the mechanism for holding the quantization feature, i.e., the quantization feature storing portion 31 is essential in the related art. Second, even if the input image are different from the supposed type, the quantization feature cannot be switched in this configuration. Where the type indicates tendency on the above frequency component. For example, in the document, the photograph, CG, etc., contained frequency components are different, respectively. Third, if fine adjustment is required for the unexpected input, e.g., incomplete resolution and incomplete image quality, it is impossible to deal with since the corresponding quantization features are not stored.
As described above, as the problems in the related art, an implementation cost, instability of the evaluation, and the non-universality, that are inhere in the sensory evaluation itself, are listed since the results of the sensory evaluations are utilized. Also, as the problems based on the configuration, cost of the additional configuration, inadequacy to the image type, and inadaptability to the variation in the parameters may be listed.